The methods employed to study cell-substrate interaction can be classified into three major categories; microscopic methods, including fluorescent microscopy, interference reflection microscopy, scanning electron microscopy, digital holographic microscopy, surface plasmon resonance microscopy, and acoustic microscopy; biochemical methods, including receptor-ligand interactions; and spectroscopic methods, including nuclear magnetic resonance and electrical impedance spectroscopy. These techniques can be broadly classified as either label- and reporter-based or label-free. Label- and reporter-based technologies require elaborate sample preparation, pre- and post-treatment. Label-free technologies are preferred due to their non-invasiveness and non-interference with biological functions.
Among the several classes of cell-based biosensors, impedemetric sensors provide a non-invasive and label-free method for monitoring cell cultures (Abassi, Y. A., et al., Label-free, real-time monitoring of IgE-mediated mast cell activation on microelectronic cell sensor arrays. J Immunol Methods, 2004. 292(1-2): p. 195-205). The feasibility of impedemetric sensors has been demonstrated for a variety of applications including wound healing, cell-drug interaction and apoptosis (Yin, H., et al., Bioelectrical Impedance Assay to Monitor Changes in Aspirin-Treated Human Colon Cancer HT-29 Cell Shape during Apoptosis. Analytical Letters, 2007. 40(1): p. 85-94; Harrison, D. J., et al., “From micromotors to micro fluidics the blossoming of micromachining technologies in chemistry, biochemistry, and biology,”. Proc Transducers. 99: p. 7-10.). Electrical impedance-based techniques offer several advantages over microscopic techniques, such as non-invasiveness, non-destructive characteristics, label free characteristics, real-time characteristics, and dynamic monitoring of cell culture systems. However, current impedance-based whole-cell biosensors technology suffers from a lack of spatial resolution, non-specific, non-parameterized quantitative descriptors of adhesion and motility, and limited data processing and analysis capabilities.
Many abnormal cell types exhibit adhesion and proliferation characteristics that are different from their normal counterparts (Buehring, G. C. and R. R. Williams, Growth Rates of Normal and Abnormal Human Mammary Epithelia in Cell Culture. Cancer Res, 1976. 36(10): p. 3742-3747). To distinguish these behavioral characteristics, a space and time resolved measurement system is needed. Current real-time impedance-based cell substrate sensors use non-specific space-averaged quantitative descriptors to represent cell substrate interaction and often do not characterize the cell layer (Yu, N., et al., Real-time monitoring of morphological changes in living cells by electronic cell sensor arrays: an approach to study G protein-coupled receptors. Anal Chem, 2006. 78(1): p. 35-43; Peters, M. F., et al., Evaluation of Cellular Dielectric Spectroscopy, a Whole-Cell, Label-Free Technology for Drug Discovery on Gi-Coupled GPCRs. J Biomol Screen, 2007). Although these descriptors provide some measure of the cellular activity, the exact nature of the cell-substrate interactions is unclear. Further, a primary hurdle in distributed impedance mapping is the additional hardware requirement of switching between electrodes without introducing measurement parasitics. Additionally, data collection, processing, analysis and visualization are difficult to implement and integrate for large multidimensional datasets, especially in a real-time environment.
Real-Time Cellular Electronic Sensing (RT-CES™), manufactured by ACEA Biosciences, is a cell culture monitoring device based on a microelectronic cell sensor array integrated into the bottom of standard format micro-titre plates. RT-CES™ measures electrical impedance across the sensors to detect the presence, absence, or change in condition of cells (Matthew, A., Current biosensor technologies in drug discovery. Drug Discovery, 2006: p. 69). The CellKey™ System (MDS Sciex) uses cellular dielectric spectroscopy (CDS) to quantitatively and kinetically measure endogenous cell surface receptor responses to ligands in live cells. A series of receptor-specific, frequency-dependent impedance patterns, resulting from changes in cellular bio impedance are collected every two seconds as spectrum of frequencies (1 KHz to 10 MHz) (Ciambrone, G. J., et al., Cellular Dielectric Spectroscopy: A Powerful New Approach to Label-Free Cellular Analysis. Journal of Biomolecular Screening, 2004. 9(6): p. 467). Legendre Polynomial fitting is used to fit the difference between control and ligand initiated impedance data.
A major challenge in IS is in the interpretation of the impedance spectra. IS data is predominantly interpreted by parameter extraction via equivalent circuit modeling. Traditional spectroscopic measurements involve few datasets which are fit to circuits of choice to extract parameters. An impedance recording of cell cultures and tissues is either determined by differential impedance or absolute impedance. In differential recording, a blank or control sample is measured and compared with the impedance (or part of it) of the analyte (Huang, X., et al., Impedance based biosensor array for monitoring mammalian cell behavior. Sensors, 2003. Proceedings of IEEE, 2003. 1). In this approach, the absolute electrical properties are unknown and only their deviations act as markers for variability. In the second category, absolute electrical properties of cells are recorded, with the view of characterizing a particular type of cell in terms of its electrical characteristics such as complex conductivity and permittivity (Asami, K., Dielectric dispersion in biological cells of complex geometry simulated by the three-dimensional finite difference method. J. Phys. D: Appl. Phys, 2006. 39: p. 492-9).
Although cell-substrate studies are capable of detecting micromotion of cells (Giaever, I. and C. R. Keese, Micromotion of Mammalian Cells Measured Electrically. Proceedings of the National Academy of Sciences, 1991. 88(17): p. 7896-7900), the observed impedance changes are an averaged effect of cellular motion over a large electrode area (compared to the cellular dimensions of approximately 30 μm). Moreover, the parameter describing the cell micromotion is non-specific. For example, due to the cell's close approach to the interface, the electrical double layer capacitance is modified, this is a specific quantifier because it refers to a particular aspect of cell-substrate interface. The existing methods quantify the cell-substrate interaction in terms of normalized resistance (Keese, C. R., et al., A biosensor that monitors cell morphology with electrical fields. Engineering in Medicine and Biology Magazine, IEEE, 1994. 13(3): p. 402-408) or a cell index (Yu, N., et al., Real-time monitoring of morphological changes in living cells by electronic cell sensor arrays: an approach to study G protein-coupled receptors. Anal Chem, 2006. 78(1): p. 35-43) (non-specific quantifiers), not in terms of changes in interfacial capacitance or cell-junctional resistance (specific quantifiers). With currently available impedance-based cell-substrate sensing methods, it is not possible to predict the directionality of cell growth and the cell density distribution, which are important indicators of “orderliness” of growth which in turn is an important distinction between normal and cancer cells (Schwartz, D. R., et al., Gene Expression in Ovarian Cancer Reflects Both Morphology and Biological Behavior, Distinguishing Clear Cell from Other Poor-Prognosis Ovarian Carcinomas. Cancer Res, 2002. 62(16): p. 4722-4729; Szent-Gyorgyi, A., The Living State and Cancer. PNAS, 1977. 74(7): p. 2844-2847).
Contemporary systems lack the capability for real-time impedance mapping and parameterization of evolving cell cultures and provide only an aggregate quantity measured over either a single frequency or a group of frequencies to represent cell-substrate interactions. A system is needed which can monitor specific parameters of cell-substrate interactions (e.g. cell-substrate separation) and cell-cell interactions (e.g. tight junctional resistance) on a real-time basis. A multiple electrode system performing impedance spectroscopy in real-time generates large datasets, where each electrode data has to be individually fit to an equivalent circuit for parameter extraction. This task is cumbersome unless automated. However, no such system has been reported yet that can perform real-time impedance mapping and automated parameter extraction from impedance data of time evolving cell cultures. A key challenge in the implementation of automated parameterization of evolving cell cultures is that the model representing the system continuously changes.